119 个 · RAG 检索增强 · AI 核心
SharpAI 是基于 llama.cpp（通过 LlamaSharp）构建的可嵌入 Embedding、补全与模型管理平台，内置 Ollama 兼容的 Web 服务。SharpAI is an embeddable embeddings, completions, and model management platform using llama.cpp via LlamaSharp, with a built-in Ollama-compatible webserver.
面向 Node.js 与 TypeScript 的 AI firewall。阻止 prompt injection、音频幻觉与 RAG 数据爬取。零依赖。MIT 许可。AI firewall for Node.js & TypeScript. Stop prompt injection, audio hallucinations, and RAG data scraping. Zero dependencies. MIT
动态 README，包含 AI 生成的 SCP 基金会与 Wikipedia 随机文章摘要。A dynamic README with AI-generated summaries of random articles from the SCP Foundation and Wikipedia.
CPersona — 持久化 AI 记忆服务器，具备 3 层混合搜索、置信度评分和 30 个工具，MIT 许可。CPersona — Persistent AI memory server with 3-layer hybrid search, confidence scoring, and 30 tools. MIT licensed.
本地优先、可审计的技术文档与源代码知识编译器。Local-first, auditable knowledge compiler for technical docs and source code
金融与生活交易学习 RAG 与 QuantConnect LEAN 回测工作流（FastAPI、Qdrant、pgvector、Docker）。금융·생활거래 학습 RAG와 QuantConnect LEAN 백테스트 워크플로우 (FastAPI, Qdrant, pgvector, Docker)
🔬 基于 AI 与研究论文对话，通过高级语义搜索与 RAG（检索增强生成）技术提取洞见与摘要。🔬 Chat with research papers using AI, extracting insights and summaries through advanced semantic search and Retrieval-Augmented Generation techniques.
用于评估检索增强视觉语言模型在循证医学视觉问答中表现的研究框架Research framework evaluating retrieval-augmented vision-language models for evidence-grounded medical visual question answering.
支持从多源智能检索、筛选与总结科学论文，提升研究与报告生成效率Enable intelligent retrieval, filtering, and summarization of scientific papers from multiple sources for efficient research and report generation.
LOCAH.ai —— 基于 Wilfrid Laurier University 公开信息的助手。提供引用标注的回答、显式呈现的矛盾，以及 Laurier 信息在何处未能服务学生的证据。LOCAH.ai — an assistant over Wilfrid Laurier University's public information. Cited answers, surfaced contradictions, and evidence on where Laurier's information fails students.
一条 RAG pipeline，从 Stack Overflow 抓取问答内容并转化为 RAG 格式，存储到 huggingface 数据集中。This is RAG pipeline that take question answer from Stack Overflow and convert it to rag and get stored in the dataset in huggingface
📊 通过构建知识图谱并从文档语料中提取洞察，简化面向查询的摘要流程——基于 GraphRAG 流水线。📊 Streamline query-focused summarization by constructing knowledge graphs and extracting insights from document corpora with the GraphRAG pipeline.
使用本地 LLM 与私密法律文档对话，在自有硬件上获得带引用、可验证的答案。Chat with private legal documents using local LLMs. Get cited, verifiable answers on your own hardware.
AI Research Wiki 2026：通过深度引用综合自动构建知识库AI Research Wiki 2026: Auto-Building Knowledge Base with Deep Citation Syntheses
基于 LlamaIndex、Redis 与 PII 脱敏的 RAG 搜索引擎。RAG search engine using LlamaIndex, Redis, and PII masking.
使用先进的 RAG 系统发现你的下一部心仪动画，提供精准推荐与增强语义搜索。🎬 Discover your next favorite anime with this advanced Retrieval-Augmented Generation system, offering precise recommendations and enriched semantic search.
将学术论文转换为带注释、浏览器友好的网页，逻辑配色清晰、导航便捷，由 OpenAlex 数据驱动。Convert academic papers into annotated, browser-friendly web pages with color-coded logic and easy navigation powered by OpenAlex data.
一个面向 PDF 文档问答的全栈 RAG 应用：上传 PDF，将其索引到本地向量库，然后基于页面级答案进行对话，并在内置阅读器中通过可点击引用跳转到对应页面。A full-stack retrieval-augmented generation (RAG) application for question answering over PDF documents. Upload a PDF, index it into a local vector store, then chat with page-grounded answers and clickable citations that jump to the right page in the built-in viewer.
基于 FastAPI + LangChain + RAG 的 AI 智能对话助手，支持多轮对话记忆、图片分析、流式回复、知识库 RAG 检索、上传定义知识库。
自托管、100% 本地的 AI 平台——在一个 Docker 栈中集成 LLM 推理、RAG 与知识图谱。无需 API key。Self-hosted, 100% local AI platform — LLM inference, RAG, and knowledge graphs in one Docker stack. No API keys.
基于 RAG 的文档问答机器人，可对任意 PDF/文本文件提问。使用本地 embeddings + Groq API。学习要点：embeddings、向量搜索、分块、RAG 流水线。Document Q&A bot using RAG (Retrieval-Augmented Generation). Ask questions about any PDF/text file. Uses local embeddings + Groq API. Learns: embeddings, vector search, chunking, RAG pipeline.
Rust 可嵌入的混合搜索原语：BM25、HNSW、reciprocal-rank fusion、UTF-8 安全的 chunking，零依赖。Embeddable hybrid search primitives for Rust: BM25, HNSW, reciprocal-rank fusion, UTF-8-safe chunking. Zero dependencies.
面向业务的 RAG | 要么有引用，要么不要 | 客服团队需要一份可核验的答案RAG for business | Citations or nothing | Support teams need an answer they can verify
🛠️ 使用 Haystack 轻松构建强大搜索系统，该框架用于开发端到端问答与搜索应用。🛠️ Build powerful search systems effortlessly with Haystack, a framework for developing end-to-end question answering and search applications.
RAG 真的物有所值吗？ragornot 在真实 AWS Lambda + Bedrock 后端上，通过四种检索模式（Flat/BM25、Hierarchical、LLM-only、RAG）运行相同查询，并测量延迟、质量、成本和碳排放——用数据帮你决定是否使用 RAG。静态 Next.js 部署于 GitHub Pages。Does RAG actually earn its cost? ragornot runs the same query through four retrieval modes (Flat/BM25, Hierarchical, LLM-only, RAG) against a live AWS Lambda + Bedrock backend and measures latency, quality, cost, and carbon — so you can decide RAG-or-not with data. Static Next.js on GitHub Pages.
在同一 chunk 集合上对比 Lexical / Vector / Graph RAG，提供确定性评估、自我修正的 LangGraph agent 循环、PII 治理与 RAG 就绪度分析器。Lexical vs Vector vs Graph RAG over one identical chunk set, with deterministic evaluation, a self-correcting LangGraph agent loop, PII governance, and a RAG-readiness analyzer.
📚 构建并评估 RAG 流水线，实现数据导入、嵌入、检索与问答，并提供准确性与相关性指标。📚 Build and evaluate RAG pipelines to ingest, embed, retrieve, and answer questions with metrics for accuracy and relevance.
为 MI Tech Arsenal 定制的基于 RAG 的 AI 助手，具备自动化 sitemap 索引、通过 ChromaDB 进行神经搜索，以及 Streamlit 到 Blogger 的无缝集成。A custom RAG-based AI assistant for MI Tech Arsenal. Features automated sitemap indexing, neural search via ChromaDB, and a seamless Streamlit-to-Blogger integration.
🛠️ 通过 ACG 增强 RAG 系统，依托可靠的外部知识提升准确性与事实一致性，减少 LLM 响应中的幻觉。🛠️ Enhance RAG systems with ACG to reduce hallucinations in LLM responses by improving accuracy and grounding in reliable external knowledge.
Semantic Search for Pi 2026：本地知识库与 AI 工具。Semantic Search for Pi 2026: Local Knowledge Base & AI Tool
使用本地 RAG 系统增强知识库，借助混合搜索实现精准信息检索与最优结果🔍 Enhance your knowledge base with a local RAG system that leverages hybrid search for precise information retrieval and optimal results.
为 Ramone 提供的容器化本地 RAG 服务，使用 atlas-corpus 检索、ChromaDB 会话记忆和 Ollama 生成。Containerised local RAG service for Ramone using atlas-corpus retrieval, ChromaDB session memory and Ollama generation.
数据在被构建成有意义的东西之前只是噪声——将原始数据转化为真正可用的系统，涵盖欺诈检测、RAG pipeline、计算机视觉追踪器以及数据仓库等领域。FAST NUCES 数据科学本科生，在"搞坏东西"和"交付产品"之间反复横跳。持续构建，持续学习，欢迎合作和实习交流Data is noise until someone builds something meaningful out of it, turn raw data into systems that actually work, from fraud detection and rag pipelines to computer vision trackers and data warehouses. DS undergrad at FAST NUCES, somewhere between breaking things and shipping them. always building, always learning, open to collabs and interns